Efficient OD Trip Matrix Prediction Based on Tensor Decomposition

Origindestination (OD) trip matrices reflect demand patterns of traffic networks and play important roles in traffic engineering. Existing approaches for traffic flow forecast such as ARIMA, SVR, and neural network perform well in modeling and predicting each OD pair separately, but they cause inefficiency when dealing with multidimensional OD demand data. In this paper, to solve the problemgiven OD trip matrices for T moments, how can we predict the overall OD trip matrices at time T+1, T+2, or even T+L, we model temporal vehicle-based OD trip matrix as a four-order tensor consisting of four attributes: origin, destination, vehicle type and time. By resorting to CANDECOMP/PARAFAC (CP) tensor decomposition, we show how a prediction method can be used to forecast future traffic demand in time factor matrix. Experiments based on a real highway tolling dataset demonstrate that our method effectively reduces the time for prediction and meanwhile makes the prediction accuracy competitive with those of other methods.